Sökning: "binary classification."

Visar resultat 21 - 25 av 231 uppsatser innehållade orden binary classification..

  1. 21. Quantum Algorithms for Feature Selection and Compressed Feature Representation of Data

    Master-uppsats, KTH/Fysik

    Författare :William Laius Lundgren; [2023]
    Nyckelord :Feature selection; autoencoders; quantum machine learning; quantum circuits; quantum annealing; Funktionsval; datakompression; kvantmaskininlärning; kvantalgoritmer; kvantkretsar;

    Sammanfattning : Quantum computing has emerged as a new field that may have the potential to revolutionize the landscape of information processing and computational power, although physically constructing quantum hardware has proven difficult,and quantum computers in the current Noisy Intermediate Scale Quantum (NISQ) era are error prone and limited in the number of qubits they contain.A sub-field within quantum algorithms research which holds potential for the NISQ era, and which has seen increasing activity in recent years, is quantum machine learning, where researchers apply approaches from classical machine learning to quantum computing algorithms and explore the interplay between the two. LÄS MER

  3. 22. An Evaluation of Classical and Quantum Kernels for Machine Learning Classifiers

    Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Teo Nordström; Jacob Westergren; [2023]
    Nyckelord :Machine Learning; Quantum Computing; Kernels; Support Vector Machines; Maskininlärning; Kvantberäkning; Kärnor; Stödvektormaskin;

    Sammanfattning : Quantum computing is an emerging field with potential applications in machine learning. This research project aimed to compare the performance of a quantum kernel to that of a classical kernel in machine learning binary classification tasks. LÄS MER

  4. 23. A Hybrid Approach to Hate Speech Detection

    Master-uppsats, Umeå universitet/Institutionen för datavetenskap

    Författare :Simon Rickardsson; [2023]
    Nyckelord :;

    Sammanfattning : An interesting question is to what extent can background knowledge help in the context of text classification. To address this in more detail, can a traditional rulebased classifier help boost the accuracy of learned models? We explore this here for detecting hate speech and offensive language in online text. LÄS MER

  5. 24. Evaluating Random Forest and k-Nearest Neighbour Algorithms on Real-Life Data Sets

    Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Atheer Salim; Milad Farahani; [2023]
    Nyckelord :Random Forest; k-Nearest Neighbour; Evaluation; Machine Learning; Classification; Execution Time; Slumpmässig Skog; k-Närmaste Granne; Utvärdering; Maskininlärning; Klassificiering; Exekveringstid;

    Sammanfattning : Computers can be used to classify various types of data, for example to filter email messages, detect computer viruses, detect diseases, etc. This thesis explores two classification algorithms, random forest and k-nearest neighbour, to understand how accurately and how quickly they classify data. LÄS MER

  7. 25. Data-Driven Traffic Forecasting for Completed Vehicle Simulation: : A Case Study with Volvo Test Trucks

    Magister-uppsats, Luleå tekniska universitet/Institutionen för system- och rymdteknik

    Författare :Samaneh Shahrokhi; [2023]
    Nyckelord :supervised machine learning; traffic forecasting; vehicle presence prediction; binary classification; ensemble learning; feature engineering; hyperparameter tuning; data-driven analysis;

    Sammanfattning : This thesis offers a thorough investigation into the application of machine learning algorithms for predicting the presence of vehicles in a traffic setting. The research primarily focuses on enhancing vehicle simulation by employing data-driven traffic prediction methods. The study approaches the problem as a binary classification task. LÄS MER